
'''simple docstring'''






UpperCAmelCase_							: Any =							[
    'Audio',
    'Array2D',
    'Array3D',
    'Array4D',
    'Array5D',
    'ClassLabel',
    'Features',
    'Sequence',
    'Value',
    'Image',
    'Translation',
    'TranslationVariableLanguages',
]
from .audio import Audio
from .features import ArrayaD, ArrayaD, ArrayaD, ArrayaD, ClassLabel, Features, Sequence, Value
from .image import Image
from .translation import Translation, TranslationVariableLanguages







'''simple docstring'''





import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer
def __UpperCAmelCase   (					A					:			Optional[int]		)    ->   List[Any]:
			# This defines a "chinese character" as anything in the CJK Unicode block:
			#   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
			#
			# Note that the CJK Unicode block is NOT all Japanese and Korean characters,
			# despite its name. The modern Korean Hangul alphabet is a different block,
			# as is Japanese Hiragana and Katakana. Those alphabets are used to write
			# space-separated words, so they are not treated specially and handled
			# like the all of the other languages.
			if (
			    (cp >= 0X4E_00 and cp <= 0X9F_FF)
			    or (cp >= 0X34_00 and cp <= 0X4D_BF)  #
			    or (cp >= 0X2_00_00 and cp <= 0X2_A6_DF)  #
			    or (cp >= 0X2_A7_00 and cp <= 0X2_B7_3F)  #
			    or (cp >= 0X2_B7_40 and cp <= 0X2_B8_1F)  #
			    or (cp >= 0X2_B8_20 and cp <= 0X2_CE_AF)  #
			    or (cp >= 0XF9_00 and cp <= 0XFA_FF)
			    or (cp >= 0X2_F8_00 and cp <= 0X2_FA_1F)  #
			):  #
						return True

			return False
def __UpperCAmelCase   (					A					:			str		)    ->   Optional[Any]:
			# word like '180' or '身高' or '神'
			for char in word:
						UpperCAmelCase_      :       str          =       ord(A		)
						if not _is_chinese_char(A		):
									return 0
			return 1
def __UpperCAmelCase   (					A					:			List[str]		)    ->   Dict:
			UpperCAmelCase_      :       Optional[Any]          =       set()

			for token in tokens:
						UpperCAmelCase_      :       str          =       len(A		) > 1 and is_chinese(A		)
						if chinese_word:
									word_set.add(A		)
			UpperCAmelCase_      :       Optional[int]          =       list(A		)
			return word_list
def __UpperCAmelCase   (					A					:			List[str]			,       A					:			set()		)    ->   Optional[Any]:
			if not chinese_word_set:
						return bert_tokens
			UpperCAmelCase_      :       Dict          =       max([len(A		) for w in chinese_word_set]		)

			UpperCAmelCase_      :       List[str]          =       bert_tokens
			UpperCAmelCase_    ,      UpperCAmelCase_      :       List[Any]          =       0, len(A		)
			while start < end:
						UpperCAmelCase_      :       str          =       True
						if is_chinese(bert_word[start]		):
									UpperCAmelCase_      :       str          =       min(end - start			,       A		)
									for i in range(A			,       1			,       -1		):
												UpperCAmelCase_      :       Tuple          =       ''''''.join(bert_word[start : start + i]		)
												if whole_word in chinese_word_set:
															for j in range(start + 1			,       start + i		):
																		UpperCAmelCase_      :       Union[str, Any]          =       '''##''' + bert_word[j]
															UpperCAmelCase_      :       Any          =       start + i
															UpperCAmelCase_      :       Optional[int]          =       False
															break
						if single_word:
									start += 1
			return bert_word
def __UpperCAmelCase   (					A					:			List[str]			,       A					:			LTP			,       A					:			BertTokenizer		)    ->   Union[str, Any]:
			UpperCAmelCase_      :       Optional[int]          =       []

			for i in range(0			,       len(A		)			,       1_0_0		):
						UpperCAmelCase_      :       int          =       ltp_tokenizer.pipeline(lines[i : i + 1_0_0]			,       tasks=['''cws''']		).cws
						UpperCAmelCase_      :       Any          =       [get_chinese_word(A		) for r in res]
						ltp_res.extend(A		)
			assert len(A		) == len(A		)

			UpperCAmelCase_      :       Tuple          =       []
			for i in range(0			,       len(A		)			,       1_0_0		):
						UpperCAmelCase_      :       Optional[int]          =       bert_tokenizer(lines[i : i + 1_0_0]			,       add_special_tokens=A			,       truncation=A			,       max_length=5_1_2		)
						bert_res.extend(res['''input_ids''']		)
			assert len(A		) == len(A		)

			UpperCAmelCase_      :       Any          =       []
			for input_ids, chinese_word in zip(A			,       A		):
						UpperCAmelCase_      :       Union[str, Any]          =       []
						for id in input_ids:
									UpperCAmelCase_      :       Union[str, Any]          =       bert_tokenizer._convert_id_to_token(A		)
									input_tokens.append(A		)
						UpperCAmelCase_      :       List[str]          =       add_sub_symbol(A			,       A		)
						UpperCAmelCase_      :       Any          =       []
						# We only save pos of chinese subwords start with ##, which mean is part of a whole word.
						for i, token in enumerate(A		):
									if token[:2] == "##":
												UpperCAmelCase_      :       int          =       token[2:]
												# save chinese tokens' pos
												if len(A		) == 1 and _is_chinese_char(ord(A		)		):
															ref_id.append(A		)
						ref_ids.append(A		)

			assert len(A		) == len(A		)

			return ref_ids
def __UpperCAmelCase   (					A					:			List[Any]		)    ->   Tuple:
			# For Chinese (Ro)Bert, the best result is from : RoBERTa-wwm-ext (https://github.com/ymcui/Chinese-BERT-wwm)
			# If we want to fine-tune these model, we have to use same tokenizer : LTP (https://github.com/HIT-SCIR/ltp)
			with open(args.file_name			,       '''r'''			,       encoding='''utf-8'''		) as f:
						UpperCAmelCase_      :       Optional[Any]          =       f.readlines()
			UpperCAmelCase_      :       List[str]          =       [line.strip() for line in data if len(A		) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
			UpperCAmelCase_      :       Tuple          =       LTP(args.ltp		)  # faster in GPU device
			UpperCAmelCase_      :       Union[str, Any]          =       BertTokenizer.from_pretrained(args.bert		)

			UpperCAmelCase_      :       Optional[int]          =       prepare_ref(A			,       A			,       A		)

			with open(args.save_path			,       '''w'''			,       encoding='''utf-8'''		) as f:
						UpperCAmelCase_      :       str          =       [json.dumps(A		) + '''\n''' for ref in ref_ids]
						f.writelines(A		)


if __name__ == "__main__":
						_UpperCamelCase						:						Optional[Any]  =					argparse.ArgumentParser(description='prepare_chinese_ref')
						parser.add_argument(
						    '--file_name',
						    required=False,
						    type=str,
						    default='./resources/chinese-demo.txt',
						    help='file need process, same as training data in lm',
						)
						parser.add_argument(
						    '--ltp',
						    required=False,
						    type=str,
						    default='./resources/ltp',
						    help='resources for LTP tokenizer, usually a path',
						)
						parser.add_argument(
						    '--bert',
						    required=False,
						    type=str,
						    default='./resources/robert',
						    help='resources for Bert tokenizer',
						)
						parser.add_argument(
						    '--save_path',
						    required=False,
						    type=str,
						    default='./resources/ref.txt',
						    help='path to save res',
						)

						_UpperCamelCase						:						Any  =					parser.parse_args()
						main(args)


